U.S. patent number 10,902,346 [Application Number 15/471,933] was granted by the patent office on 2021-01-26 for efficient semi-supervised concept organization accelerated via an inequality process.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Alfredo Alba, Kenneth L. Clarkson, Clemens Drews, Ronald Fagin, Daniel F. Gruhl, Neal R. Lewis, Pablo N. Mendes, Meenakshi Nagarajan, Cartic Ramakrishnan.
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United States Patent |
10,902,346 |
Alba , et al. |
January 26, 2021 |
Efficient semi-supervised concept organization accelerated via an
inequality process
Abstract
One embodiment provides generating a similarity matrix
corresponding to an input collection including initializing, by a
processor, a working set as a collection of a multiple items. Until
the similarity matrix converges: receiving a seed for similarity
for at least one pair of items of the multiple items, and obtaining
a similarity value for all other item pairs using a Naive Triangle
Inequality process. The similarity is generated with obtained
similarity values.
Inventors: |
Alba; Alfredo (Morgan Hill,
CA), Clarkson; Kenneth L. (Madison, NJ), Drews;
Clemens (San Jose, CA), Fagin; Ronald (Los Gatos,
CA), Gruhl; Daniel F. (San Jose, CA), Lewis; Neal R.
(San Jose, CA), Mendes; Pablo N. (San Francisco, CA),
Nagarajan; Meenakshi (San Jose, CA), Ramakrishnan;
Cartic (San Jose, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Appl.
No.: |
15/471,933 |
Filed: |
March 28, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180285762 A1 |
Oct 4, 2018 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N
5/022 (20130101); G06N 20/00 (20190101) |
Current International
Class: |
G06N
20/00 (20190101); G06N 5/02 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Slonim, Noam, and Naftali Tishby. "Document clustering using word
clusters via the information bottleneck method." Proceedings of the
23rd annual international ACM SIGIR conference on Research and
development in information retrieval. ACM, 2000: 208-215 (Year:
2000). cited by examiner .
Dhillon, Inderjit S., and Dharmendra S. Modha. "Concept
decompositions for large sparse text data using clustering."
Machine learning 42.1-2 (2001): 143-175. (Year: 2001). cited by
examiner .
Ahmed, Eya Ben, Ahlem Nabli, and Faiez Gargouri. "SHACUN:
Semi-supervised Hierarchical Active Clustering Based on Ranking
Constraints." Industrial Conference on Data Mining. Springer,
Berlin, Heidelberg, 2012: 194-208 (Year: 2012). cited by examiner
.
Low, Yucheng, and Alice X. Zheng. "Fast top-k similarity queries
via matrix compression." Proceedings of the 21st ACM international
conference on Information and knowledge management. ACM, 2012.
(Year: 2012). cited by examiner .
Basu, Tanmay, and C. A. Murthy. "A similarity based supervised
decision rule for qualitative improvement of text categorization."
Fundamenta Informaticae 141.4 (2015): 275-295, pp. 1-21. (Year:
2015). cited by examiner .
Zheng, Li, and Tao Li. "Semi-supervised hierarchical clustering."
2011 IEEE 11th International Conference on Data Mining. IEEE:
982-991 (Year: 2011). cited by examiner .
Mell, P., et al., "The NIST Definition of Cloud Computing",
National Institute of Standards and Technology Special Publication
800-145, Sep. 2011, pp. 1-7, U.S. Department of Commerce, United
States. cited by applicant.
|
Primary Examiner: Afshar; Kamran
Assistant Examiner: Baldwin; Randall K.
Attorney, Agent or Firm: Sherman IP LLP Sherman; Kenneth L.
Laut; Steven
Claims
What is claimed is:
1. A method for generating a similarity matrix corresponding to an
input collection comprising: initializing, by a processor, a
working set as a collection of a plurality of items; until the
similarity matrix converges based on a particular test of
convergence: receiving a seed for similarity for at least one pair
of items of the plurality of items; and obtaining, using the
processor, a similarity value for all other item pairs using a
Naive Triangle Inequality process that uses a maximum distance
matrix Dmax(j, k) and a minimum distance matrix Dmin(i, k),
determines a best guess matrix B and an uncertainty matrix U(i, j)
from the minimum distance matrix Dmin (i, k) and the maximum
distance matrix Dmax(j, k), and determines a distance matrix D,
wherein i, j and k are positive integers; swapping an upper bound
value for Dmax and a lower bound value for Dmin upon Dmin(i,
k)>Dmax(j, k); and generating, by the processor, the similarity
matrix with obtained similarity values based on the Naive Triangle
Inequality process.
2. The method of claim 1, further comprising: providing the
generated similarity matrix for clustering processing.
3. The method of claim 2, wherein the plurality of items comprises
one of words and phrases.
4. The method of claim 3, further comprising: searching, by the
processor, for cells in the uncertainty matrix U(i, j) for a
maximum uncertainty value, and randomly selecting a cell with the
maximum uncertainty value; and updating, by the processor,
corresponding cells for Dmin (i, j), Dmin (j, i), Dmax(i, j) and
Dmax(j, i); wherein the seed for similarity for the at least one
pair of items is received by the processor from a subject matter
expert (SME) via a user interface in response to the random
selection of the cell with the maximum uncertainty value.
5. The method of claim 1, wherein the similarity matrix comprises
similarity values between items, and each cell in the similarity
matrix represents the similarity between row and column items.
6. The method of claim 5, wherein cells in the distance matrix D
represent distance between an item represented by a row and an item
represented by a column.
7. The method of claim 6, wherein the Naive Triangle Inequality
process performs distance bound updates for cells in the similarity
matrix for which a corresponding cell value in a Boolean matrix is
set to a value representing a true state.
8. A computer program product for generating a similarity matrix
corresponding to an input collection, the computer program product
comprising a computer readable storage medium having program
instructions embodied therewith, the program instructions
executable by a processor to cause the processor to: initialize, by
the processor, a working set as a collection of a plurality of
items; until the similarity matrix converges: receive, by the
processor, a seed for similarity for at least one pair of items of
the plurality of items; and obtain, by the processor, a similarity
value for all other item pairs using a Naive Triangle Inequality
process that uses a maximum distance matrix Dmax(j, k) and a
minimum distance matrix Dmin(i, k), determines a best guess matrix
B and an uncertainty matrix U(i, j) from the minimum distance
matrix Dmin (i, k) and the maximum distance matrix Dmax(j, k), and
determines a distance matrix D, wherein i, j and k are positive
integers; swap, by the processor, an upper bound value for Dmax and
a lower bound value for Dmin upon Dmin(i, k)>Dmax(j, k); and
generate, by the processor, the similarity matrix with obtained
similarity values based on the Naive Triangle Inequality
process.
9. The computer program product of claim 8, wherein the program
instructions executable by the processor to further cause the
processor to: provide, by the processor, the generated similarity
matrix for clustering processing.
10. The computer program product of claim 9, wherein the plurality
of items comprises one of words and phrases.
11. The computer program product of claim 10, wherein: the program
instructions executable by the processor further cause the
processor to: search, by the processor, for cells in the
uncertainty matrix U(i, j) for a maximum uncertainty value, and
randomly selecting a cell with the maximum uncertainty value; and
update, by the processor, corresponding cells for Dmin (i, j), Dmin
(j, i), Dmax(i, j) and Dmax(j, i); and the seed for similarity for
the at least one pair of items is received by the processor from a
subject matter expert (SME) via a user interface in response to the
random selection of the cell with the maximum uncertainty
value.
12. The computer program product of claim 11, wherein the
similarity matrix comprises similarity values between items, and
each cell in the similarity matrix represents the similarity
between row and column items.
13. The computer program product of claim 12, wherein cells in the
distance matrix D represent distance between an item represented by
a row and an item represented by a column.
14. The computer program product of claim 13, wherein the Naive
Triangle Inequality process performs distance bound updates for
cells in the similarity matrix for which a corresponding cell value
in a Boolean matrix is set to a value representing a true
state.
15. An apparatus comprising: a memory configured to store
instructions; and a processor configured to execute the
instructions to: initialize, by the processor, a working set as a
collection of a plurality of items; until the similarity matrix
converges: receive a seed for similarity for at least one pair of
items of the plurality of items; and obtain a similarity value for
all other item pairs using a Naive Triangle Inequality process that
uses a maximum distance matrix Dmax(j, k) and a minimum distance
matrix Dmin(i, k), determines a best guess matrix B and an
uncertainty matrix U(i, j) from the minimum distance matrix Dmin
(i, k) and the maximum distance matrix Dmax(j, k), and determines a
distance matrix D, wherein i, j and k are positive integers; swap
an upper bound value for Dmax and a lower bound value for Dmin upon
Dmin(i, k)>Dmax(j, k); and generate the similarity matrix with
obtained similarity values based on the Naive Triangle Inequality
process.
16. The apparatus of claim 15, wherein the processor is further
configured to execute the instructions to: provide the generated
similarity matrix for clustering processing.
17. The apparatus of claim 16, wherein the plurality of items
comprises one of words and phrases.
18. The apparatus of claim 17, wherein: the processor is further
configured to execute the instructions to: search for cells in the
uncertainty matrix U(i, j) for a maximum uncertainty value, and
randomly selecting a cell with the maximum uncertainty value; and
update corresponding cells for Dmin (i, j), Dmin (j, i), Dmax(i, j)
and Dmax(j, i); the seed for similarity for the at least one pair
of items is received by the processor from a subject matter expert
(SME) via a user interface in response to the random selection of
the cell with the maximum uncertainty value; the similarity matrix
comprises similarity values between items; and each cell in the
similarity matrix represents the similarity between row and column
items.
19. The apparatus of claim 15, wherein the cells in the distance
matrix D represent distance between an item represented by a row
and an item represented by a column.
20. The apparatus of claim 19, wherein the Naive Triangle
Inequality process performs distance bound updates for cells in the
similarity matrix for which a corresponding value in a Boolean
matrix is set to a value representing a true state.
Description
BACKGROUND
Many applications rely on terminologies that can be organized into
hierarchical structures. Concept hierarchies help manage
complexity, by hiding details when appropriate, but allowing users
to delve into detail when necessary; they can also provide insight
into the inter-relationships between terms, and have other uses as
well. On the one hand, as often argued in the knowledge
organization literature (including library sciences, Ontology and
Terminology), it is necessary to involve people in crafting concept
hierarchies based on our understanding of the fundamental
properties or intended use of those hierarchies. On the other hand,
research in hierarchical clustering methods has yielded ways to
create concept hierarchies from the data automatically, in a
bottom-up fashion.
SUMMARY
Embodiments relate to generating a similarity matrix corresponding
to an input collection. One embodiment includes generating a
similarity matrix corresponding to an input collection including
initializing, by a processor, a working set as a collection of a
multiple items. Until the similarity matrix converges: receiving a
seed for similarity for at least one pair of items of the multiple
items, and obtaining a similarity value for all other item pairs
using a Naive Triangle Inequality process. The similarity is
generated with obtained similarity values.
These and other features, aspects and advantages of the present
invention will become understood with reference to the following
description, appended claims and accompanying figures.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts a cloud computing environment, according to an
embodiment;
FIG. 2 depicts a set of abstraction model layers, according to an
embodiment;
FIG. 3 is a network architecture for efficient representation,
access and modification of variable length data objects, according
to an embodiment;
FIG. 4 shows a representative hardware environment that may be
associated with the servers and/or clients of FIG. 1, according to
an embodiment;
FIG. 5 is a block diagram illustrating system for generating a
similarity matrix corresponding to an input collection, according
to one embodiment; and
FIG. 6 illustrates a block diagram for a process for generating a
similarity matrix corresponding to an input collection, according
to one embodiment.
DETAILED DESCRIPTION
The descriptions of the various embodiments have been presented for
purposes of illustration, but are not intended to be exhaustive or
limited to the embodiments disclosed. Many modifications and
variations will be apparent to those of ordinary skill in the art
without departing from the scope and spirit of the described
embodiments. The terminology used herein was chosen to best explain
the principles of the embodiments, the practical application or
technical improvement over technologies found in the marketplace,
or to enable others of ordinary skill in the art to understand the
embodiments disclosed herein.
It is understood in advance that although this disclosure includes
a detailed description of cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
One or more embodiments provide convergence of a similarity matrix
that is guided by human domain-experts and a Naive Triangle
inequality process to ensure minimization of the number of
decisions that a human expert has to make in order to achieve
convergence. In one embodiment, a method for generating a
similarity matrix corresponding to an input collection includes
initializing, by a processor, a working set as a collection of a
multiple items. Until the similarity matrix converges: receiving a
seed for similarity for at least one pair of items of the multiple
items, and obtaining a similarity value for all other item pairs
using a Naive Triangle Inequality process. The similarity is
generated with obtained similarity values.
In one or more embodiments, "concept distance" provides that some
pairs of concepts are more similar to each other than other pairs.
In a human-driven class of approaches, the concept distances are
often implicit or subjectively estimated, while in data-driven
approaches, concept distances are automatically computed from input
data. One or more embodiments provide a way to leverage input from
both classes of approaches (i.e., human-driven class approaches and
data-driven class approaches). One or more embodiments benefit from
user expertise while reducing the manual effort needed to obtain
the desired concept hierarchy. In one embodiment, a process
performs automatic bottom-up calculations, using data as input, and
validates key decisions through receiving human top-down input.
Human input from a Subject Matter Expert (SME) guides the
recalculation, with the search space dramatically reduced through
our Naive Triangle Inequality (NTI) processing. As a result of the
application of the NTI processing, human effort is reduced by
having the processing automate several decisions, and algorithmic
accuracy is incrementally improved receiving input.
Business case understanding the conceptual hierarchy of objects is
a critical part of any artificial intelligence (AI) or machine
learning (ML) application. Some examples are: document queries may
be expanded or narrowed using the similarity between two words;
analytics that identify particular aspects of sentiment in a
product review need to understand the semantic similarity between
the products' names and their models; knowing that an image of a
car is dissimilar to an image of a motorcycle, yet that both can be
classified as a vehicle, is critical for defense and law
enforcement applications. Such applications provide a distinctive
business advantage in cognitive computing, because of their
increased accuracy and learning ability. One or more embodiments,
provide a process to quickly and accurately quantify and detect the
boundaries of similarity between large sets of objects. By
detecting similarity boundaries, applications will not only be able
to more accurately detect similarity, they will be able to do it
quickly, providing accurate and fast response to critical cognitive
computing.
One or more embodiments prescribes a different technique to
evaluate the weaknesses of the conceptual models via the NTI, where
the elements reporting most extreme values are selected first to
collect feedback as they are elements whose feedback convey the
most information to the model. By using the NTI processing, the
system is capable of identifying the concepts for which human
feedback would convey the most knowledge. By this mechanism, one or
more embodiments assures that the most informative feedback is
collected at the earliest in the process. By collecting the most
informative feedback sooner, the system significantly shortens the
model convergence time.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines (VMs), and services) that can be rapidly
provisioned and released with minimal management effort or
interaction with a provider of the service. This cloud model may
include at least five characteristics, at least three service
models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed and automatically, without requiring human interaction with
the service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous, thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
data center).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned and, in some cases, automatically, to quickly scale out
and rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active consumer accounts). Resource
usage can be monitored, controlled, and reported, thereby providing
transparency for both the provider and consumer of the utilized
service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is the ability to use the provider's applications running
on a cloud infrastructure. The applications are accessible from
various client devices through a thin client interface, such as a
web browser (e.g., web-based email). The consumer does not manage
or control the underlying cloud infrastructure including network,
servers, operating systems, storage, or even individual application
capabilities, with the possible exception of limited
consumer-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is the ability to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application-hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is the ability to provision processing, storage, networks,
and other fundamental computing resources where the consumer is
able to deploy and run arbitrary software, which can include
operating systems and applications. The consumer does not manage or
control the underlying cloud infrastructure but has control over
operating systems, storage, deployed applications, and possibly
limited control of select networking components (e.g., host
firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load balancing between clouds).
A cloud computing environment is a service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, an illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as private, community, public, or
hybrid clouds as described hereinabove, or a combination thereof.
This allows the cloud computing environment 50 to offer
infrastructure, platforms, and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 54A-N shown in FIG. 2 are intended to be illustrative only
and that computing nodes 10 and cloud computing environment 50 can
communicate with any type of computerized device over any type of
network and/or network addressable connection (e.g., using a web
browser).
Referring now to FIG. 2, a set of functional abstraction layers
provided by the cloud computing environment 50 (FIG. 1) is shown.
It should be understood in advance that the components, layers, and
functions shown in FIG. 2 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, a management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and pricing 82 provide cost tracking as resources are
utilized within the cloud computing environment and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and generating
a similarity matrix corresponding to an input collection processing
96. As mentioned above, all of the foregoing examples described
with respect to FIG. 2 are illustrative only, and the invention is
not limited to these examples.
It is understood all functions of one or more embodiments as
described herein may be typically performed by the processing
system 300 (FIG. 3) or the autonomous cloud environment 410 (FIG.
4), which can be tangibly embodied as hardware processors and with
modules of program code. However, this need not be the case for
non-real-time processing. Rather, for non-real-time processing the
functionality recited herein could be carried out/implemented
and/or enabled by any of the layers 60, 70, 80 and 90 shown in FIG.
2.
It is reiterated that although this disclosure includes a detailed
description on cloud computing, implementation of the teachings
recited herein are not limited to a cloud computing environment.
Rather, the embodiments of the present invention may be implemented
with any type of clustered computing environment now known or later
developed.
FIG. 3 illustrates a network architecture 300, in accordance with
one embodiment. As shown in FIG. 3, a plurality of remote networks
302 are provided, including a first remote network 304 and a second
remote network 306. A gateway 301 may be coupled between the remote
networks 302 and a proximate network 308. In the context of the
present network architecture 300, the networks 304, 306 may each
take any form including, but not limited to, a LAN, a WAN, such as
the Internet, public switched telephone network (PSTN), internal
telephone network, etc.
In use, the gateway 301 serves as an entrance point from the remote
networks 302 to the proximate network 308. As such, the gateway 301
may function as a router, which is capable of directing a given
packet of data that arrives at the gateway 301, and a switch, which
furnishes the actual path in and out of the gateway 301 for a given
packet.
Further included is at least one data server 314 coupled to the
proximate network 308, which is accessible from the remote networks
302 via the gateway 301. It should be noted that the data server(s)
314 may include any type of computing device/groupware. Coupled to
each data server 314 is a plurality of user devices 316. Such user
devices 316 may include a desktop computer, laptop computer,
handheld computer, printer, and/or any other type of
logic-containing device. It should be noted that a user device 311
may also be directly coupled to any of the networks in some
embodiments.
A peripheral 320 or series of peripherals 320, e.g., facsimile
machines, printers, scanners, hard disk drives, networked and/or
local storage units or systems, etc., may be coupled to one or more
of the networks 304, 306, 308. It should be noted that databases
and/or additional components may be utilized with, or integrated
into, any type of network element coupled to the networks 304, 306,
308. In the context of the present description, a network element
may refer to any component of a network.
According to some approaches, methods and systems described herein
may be implemented with and/or on virtual systems and/or systems,
which emulate one or more other systems, such as a UNIX system that
emulates an IBM z/OS environment, a UNIX system that virtually
hosts a MICROSOFT WINDOWS environment, a MICROSOFT WINDOWS system
that emulates an IBM z/OS environment, etc. This virtualization
and/or emulation may be implemented through the use of VMWARE
software in some embodiments.
FIG. 4 shows a representative hardware system 400 environment
associated with a user device 316 and/or server 314 of FIG. 3, in
accordance with one embodiment. In one example, a hardware
configuration includes a workstation having a central processing
unit 410, such as a microprocessor, and a number of other units
interconnected via a system bus 412. The workstation shown in FIG.
4 may include a Random Access Memory (RAM) 414, Read Only Memory
(ROM) 416, an I/O adapter 418 for connecting peripheral devices,
such as disk storage units 420 to the bus 412, a user interface
adapter 422 for connecting a keyboard 424, a mouse 426, a speaker
428, a microphone 432, and/or other user interface devices, such as
a touch screen, a digital camera (not shown), etc., to the bus 412,
communication adapter 434 for connecting the workstation to a
communication network 435 (e.g., a data processing network) and a
display adapter 436 for connecting the bus 412 to a display device
438.
In one example, the workstation may have resident thereon an
operating system, such as the MICROSOFT WINDOWS Operating System
(OS), a MAC OS, a UNIX OS, etc. In one embodiment, the system 400
employs a POSIX.RTM. based file system. It will be appreciated that
other examples may also be implemented on platforms and operating
systems other than those mentioned. Such other examples may include
operating systems written using JAVA, XML, C, and/or C++ language,
or other programming languages, along with an object oriented
programming methodology. Object oriented programming (OOP), which
has become increasingly used to develop complex applications, may
also be used.
FIG. 5 is a block diagram illustrating a system 500 for generating
a similarity matrix corresponding to an input collection, according
to one embodiment. In one embodiment, the system 500 includes
client devices 510 (e.g., mobile devices, smart devices, computing
systems, etc.), a cloud or resource sharing environment 520, and
servers 530. In one embodiment, the client devices are provided
with cloud services from the servers 530 through the cloud or
resource sharing environment 520.
In one embodiment, system 500 provides NTI processing (e.g., by the
client devices 510, the cloud or resource sharing environment, one
or more servers 530, or any combination) that uses several
matrices, including a similarity matrix S, and a distance matrix D.
The matrix S contains similarities between items (each cell is the
similarity between the row and column items). In one embodiment,
"not at all similar" is represented by 1, and "exactly similar" is
represented by 10. The diagonal entries of S are thus 10, and the
matrix is symmetric. In the distance matrix D, the cells are the
distance between the item represented by the row and item
represented by the column. Trivially a definition of D=10-S may be
employed. Since initially the real values for S (and hence, D) are
unknown, two matrices are required, Dmax and Dmin, that store the
maximum and minimum values presumed that D could take. In cases
where there is a known value (provided as input), these are the
same. If nothing is known the values are 9 (Dmax) and 0 (Dmin) for
upper and lower bounds.
In one embodiment, at any given time, the Best Guess matrix B may
be determined as B=0.5*(Dmin+Dmax). At any given time, the
uncertainty matrix U may be determined as U=Dmax-Dmin. Assume there
is a (human) oracle that can provide the system 500 an exact value
of a cell in S at any time, although there is a cost associated
with querying the oracle. Furthermore, assume that there is a
boolean matrix not_user_set, with all entries initialized to true.
Each entry not_use_set[k][k'] in this boolean matrix represents
whether the oracle has yet provided an exact value for the
similarity S[k][k']. One embodiment queries the oracle for such
exact values, guided by picking cells for which the uncertainty is
maximum. This new information, for one pair of items, is then used
to sharpen the distance bounds for other pairs of items. One
embodiment uses the triangle inequality for this purpose: it
provides that for any i, j, and k, that the distance D(i, k) is at
most D(i, j)+D(j, k), where i, j and k are positive integers. The
triangle inequality holds for points in the plane, and in many
other settings, and fits intuition about distance and similarity in
general. Applying the triangle inequality, and that Dmin and Dmax
hold, then for example D(i, j)>=D(i, k)-D(j, k)>=Dmin(i,
k)-Dmax(j, k). One embodiment uses such reasoning, together with
conditions that all distances are at least zero and at most
nine.
With noise and human inconsistency, however, the triangle
inequality may not apply, and the inferred bounds may have
Dmin>Dmax as a result. Here it is coped by simply swapping these
bounds. It should be noted that processing in the system 500
performs the distance bound updates only for those cells in the
similarity matrix S, for which the corresponding value in the
not_user_set matrix is true. This ensures that exact values
provided by the oracle are not overwritten. One portion of the NTI
processing proceeds as follows. Such processing portions are
repeated until some desired test of convergence is satisfied, such
as all U[i, j] are zero. Initially, Dmax is set to 9 and Dmin to 0
for all cells and all values in not_user_set to true. The
processing proceeds as follows:
(I) Search for cells where U is at a max. Randomly select one such
max cell, U[i, j].
(II) Provide a query (e.g., through a user interface, display,
etc.) to the oracle for an exact value for that cell in S.
(III) Update the corresponding cells Dmin[i, j] and Dmin[j, i], and
the corresponding cells Dmax[i, j] and Dmax[j, i], with this new
data; these will be the same since the exact value in S is
known.
(IV)
TABLE-US-00001 not_user_set[i, j] = false, // update Dmax values
for k, k' in 1...n if not_user_set[k, k'] then Dmax[k, k'] := min{
Dmax{k, k'], Dmax[k, i] + Dmax[i, j] + Dmax[j, k']} // update Dmin
values for k, k', k'' in 1...n if not_user_set[k, k'] then Dmin[k,
k'] = max{ Dmin[k, k'], Dmin[k, k''] - Dmax[k'', k'] } // maintain
Dmin < Dmax for all pairs for k, k' in 1...m if Dmin[k, k'] >
Dmax[k, k'] then swap them.
In one embodiment, a running example is presented as follows.
Imagine a doctor is trying to categorize ten (10) items used in a
minor injury clinic as follows:
0=>"bacitracin",
1=>"bandaids",
2=>"gauzepad",
3=>"ibuprofen",
4=>"naproxen",
5=>"neosporin",
6=>"polymyxin",
7=>"polysporin",
8=>"sterristrips",
9=>"sutures"
Conventionally, the Doctor will categorize the ten items by
answering pointwise questions about how similar she feels any two
items are. Note that while this example is for ease of explanation
and only has ten items, one could just "glance at it" and figure
out the categorization. With 200-2000 or more items, however, the
problem could become unfeasible. In one example embodiment, the
process starts with (I) and determines that since there is no
information on the terms at all, any pair (besides reflexive ones)
is randomly selected. The processing in system 500 selects how
similar are "polymyxin" and "sterristrips." The system 500 receives
a reply to a query to the SME oracle of "1", meaning not at all
similar (II). The process updates (III) Dmin and Dmax for the cells
[6, 8] and [8, 6] to 9 and 9 (since there is an exact value). Now
the processing goes through all the cells. Consider 0, 8--the
similarity of sterristrips and bacitracin. (IV) The update loop for
Dmax doesn't change Dmax[0, 8], since D[i, j]=9 when i=6 and j=8.
The update loop for Dmin does not change Dmin[0, 8], since all
Dmax's remain at 9. But consider later the processing receives
information that bandaids are very similar (8) to sterristrips, so
that Dmax[1, 8]=Dmin[1, 8]=2. When the processing computes the
unknown bandaids to polymyxin now, it is determined that Dmax[6, 1]
is unchanged, but Dmin[6, 1]=max{Dmin[6, 1], Dmin[6, 8]-Dmax[8,
1]}=max{0, 9-2}=7. This information provides that the similarity
between bandaids and polymixin is probably in the range from 1 to 3
(that is, not very similar). Using the baseline approach, the
Doctor will need to answer 32 such questions before the similarity
matrix converges. In one embodiment, using NTI it will only take 14
questions. Once this similarity matrix is set, the system can use a
clustering algorithm such as k-median and break this set into
ibuprofen[3], naproxen[4], bandaids[1], gauzepad[2],
sterristrips[8], sutures[9], bacitracin[0], neosporin[5],
polymyxin[6], polysporin[7], as expected.
FIG. 6 illustrates a block diagram for a process 600 for generating
a similarity matrix corresponding to an input collection, according
to one embodiment. In block 610, process 600 initializes, by a
processor, a working set as a collection of a multiple items (e.g.,
words, phrases, etc.). In block 620, until the similarity matrix
converges, process 600 provides for receiving a seed for similarity
for at least one pair of items of the multiple items, and obtaining
a similarity value for all other item pairs using a Naive Triangle
Inequality process. In block 630, process 600 provides for
generating the similarity matrix with obtained similarity
values.
In one embodiment, in process 600 the generated similarity matrix
is provided (e.g., to a computing device, processor and memory,
etc.) for clustering processing. In one embodiment, in process 600
the seed for similarity for the at least one pair of items is
received by the processor from an SME via a user interface.
In one embodiment, in process 600 the similarity matrix includes
similarity values between items, and each cell in the similarity
matrix represents the similarity between row and column items. In
one embodiment, the Naive Triangle Inequality process uses a
distance matrix, and cells in the distance matrix represent
distance between an item represented by a row and an item
represented by a column. In one embodiment, in process 600 the
Naive Triangle Inequality process performs distance bound updates
for cells in the similarity matrix for which a corresponding value
in a not_user_set matrix is true.
As will be appreciated by one skilled in the art, aspects of the
present invention may be embodied as a system, method or computer
program product. Accordingly, aspects of the present invention may
take the form of an entirely hardware embodiment, an entirely
software embodiment (including firmware, resident software,
micro-code, etc.) or an embodiment combining software and hardware
aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
Any combination of one or more computer readable medium(s) may be
utilized. The computer readable medium may be a computer readable
signal medium or a computer readable storage medium. A computer
readable storage medium may be, for example, but not limited to, an
electronic, magnetic, optical, electromagnetic, infrared, or
semiconductor system, apparatus, or device, or any suitable
combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
A computer readable signal medium may include a propagated data
signal with computer readable program code embodied therein, for
example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of
the present invention may be written in any combination of one or
more programming languages, including an object oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
Aspects of the present invention are described below with reference
to flowchart illustrations and/or block diagrams of methods,
apparatus (systems) and computer program products according to
embodiments of the invention. It will be understood that each block
of the flowchart illustrations and/or block diagrams, and
combinations of blocks in the flowchart illustrations and/or block
diagrams, can be implemented by computer program instructions.
These computer program instructions may be provided to a processor
of a general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the
computer or other programmable data processing apparatus, create
means for implementing the functions/acts specified in the
flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
References in the claims to an element in the singular is not
intended to mean "one and only" unless explicitly so stated, but
rather "one or more." All structural and functional equivalents to
the elements of the above-described exemplary embodiment that are
currently known or later come to be known to those of ordinary
skill in the art are intended to be encompassed by the present
claims. No claim element herein is to be construed under the
provisions of 35 U.S.C. section 112, sixth paragraph, unless the
element is expressly recited using the phrase "means for" or "step
for."
The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
The corresponding structures, materials, acts, and equivalents of
all means or step plus function elements in the claims below are
intended to include any structure, material, or act for performing
the function in combination with other claimed elements as
specifically claimed. The description of the present invention has
been presented for purposes of illustration and description, but is
not intended to be exhaustive or limited to the invention in the
form disclosed. Many modifications and variations will be apparent
to those of ordinary skill in the art without departing from the
scope and spirit of the invention. The embodiment was chosen and
described in order to best explain the principles of the invention
and the practical application, and to enable others of ordinary
skill in the art to understand the invention for various
embodiments with various modifications as are suited to the
particular use contemplated.
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